Neural adjustments to chromatic blur

Webster, Michael A.; Mizokami, Yoko; Svec, Leedjia; Elliott, Sarah

doi:10.1163/156856806776923380

Cited by 16 publications

(8 citation statements)

References 44 publications

(49 reference statements)

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“…For example, natural images have a characteristic 1/f amplitude spectrum. Adaptation to this structure selectively peduces sensitivity at lower frequencies, so that the effective contrast sensitivity function is more bandpass (Webster and Miyahara, 1997, Bex et al, 2009), even for chromatic contrast (which is normally taken to be lowpass) (Webster et al, 2006). The relative sensitivity to luminance and chromatic contrast also reflects adaptation to the world.…”

Section: Adapting To the Environmentmentioning

confidence: 99%

Visual Adaptation

Webster

2015

Annu. Rev. Vis. Sci.

320

291

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Sensory systems continuously mold themselves to the widely varying contexts in which they must operate. Studies of these adaptations have played a long and central role in vision science. In part this is because the specific adaptations remain a powerful tool for dissecting vision, by exposing the mechanisms that are adapting. That is, “if it adapts, it's there.” Many insights about vision have come from using adaptation in this way, as a method. A second important trend has been the realization that the processes of adaptation are themselves essential to how vision works, and thus are likely to operate at all levels. That is, “if it's there, it adapts.” This has focused interest on the mechanisms of adaptation as the target rather than the probe. Together both approaches have led to an emerging insight of adaptation as a fundamental and ubiquitous coding strategy impacting all aspects of how we see.

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Section: Adapting To the Environmentmentioning

confidence: 99%

Visual Adaptation

Webster

2015

Annu. Rev. Vis. Sci.

320

291

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“…For example, adaptation to optically induced blur has an effect on acuity (George & Rosenfield, 2004; Mon-Williams, Tresilian, Strang, Kochhar, & Wann, 1998; Pesudovs & Brennan, 1993; Rajeev & Metha, 2010) and contrast sensitivity (Mon-Williams et al, 1998; Rajeev & Metha, 2010); and adapting to images with varying levels of blur induces strong biases in the shape of the contrast sensitivity function measured both psychophysically (Webster & Miyahara, 1997; Webster, Mizokami, Svec, & Elliott, 2006) and in single cells in primary visual cortex (Sharpee et al, 2006). Moreover, adaptation to blurred images has a dramatic effect on the appearance of blur (Battaglia, Jacobs, & Aslin, 2004; Elliott, Hardy, Webster, & Werner, 2007; Vera-Diaz, Woods, & Peli, 2010; Webster, Georgeson, & Webster, 2002; Webster et al, 2006). Specifically, after adapting to images that are blurred (or sharpened) by “distorting” the ratio of low to high spatial frequency content, a physically focused image appears too sharp (or blurred), so that the point of best subjective focus is shifted toward the prevailing frequency content of the adapting images.…”

Section: Introductionmentioning

confidence: 99%

Response normalization and blur adaptation: Data and multi-scale model

2011

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Adapting to blurred or sharpened images alters perceived blur of a focused image (M. A. Webster, M. A. Georgeson, & S. M. Webster, 2002). We asked whether blur adaptation results in (a) renormalization of perceived focus or (b) a repulsion aftereffect. Images were checkerboards or 2-D Gaussian noise, whose amplitude spectra had (log–log) slopes from −2 (strongly blurred) to 0 (strongly sharpened). Observers adjusted the spectral slope of a comparison image to match different test slopes after adaptation to blurred or sharpened images. Results did not show repulsion effects but were consistent with some renormalization. Test blur levels at and near a blurred or sharpened adaptation level were matched by more focused slopes (closer to 1/f) but with little or no change in appearance after adaptation to focused (1/f) images. A model of contrast adaptation and blur coding by multiple-scale spatial filters predicts these blur aftereffects and those of Webster et al. (2002). A key proposal is that observers are pre-adapted to natural spectra, and blurred or sharpened spectra induce changes in the state of adaptation. The model illustrates how norms might be encoded and recalibrated in the visual system even when they are represented only implicitly by the distribution of responses across multiple channels.

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“…Therefore, the subjective neutral point for image focus is shifted toward the sharpness or blur level of the adapting images. These aftereffects may reflect natural variants of the spatially selective adjustments studied extensively in the context of spatial frequency adaptation (Blakemore & Campbell, 1969; Blakemore & Sutton, 1969) and occur and can be selective for different types of images, for luminance or chromatic blur, spatial or temporal blur, and to different simulated depth planes (Battaglia, Jacobs, & Aslin, 2003; Bilson, Mizokami, & Webster, 2005; Webster et al, 2002; Webster, Mizokami, Svec, & Elliott, 2006), and thus, visual coding can readily adapt to many aspects of image blur.…”

Section: Introductionmentioning

confidence: 99%

Adapting to blur produced by ocular high-order aberrations

et al. 2011

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The perceived focus of an image can be strongly biased by prior adaptation to a blurred or sharpened image. We examined whether these adaptation effects can occur for the natural patterns of retinal image blur produced by high-order aberrations (HOAs) in the optics of the eye. Focus judgments were measured for 4 subjects to estimate in a forced choice procedure (sharp/blurred) their neutral point after adaptation to different levels of blur produced by scaled increases or decreases in their HOAs. The optical blur was simulated by convolution of the PSFs from the 4 different HOA patterns, with Zernike coefficients (excluding tilt, defocus, and astigmatism) multiplied by a factor between 0 (diffraction limited) and 2 (double amount of natural blur). Observers viewed the images through an Adaptive Optics system that corrected their aberrations and made settings under neutral adaptation to a gray field or after adapting to 5 different blur levels. All subjects adapted to changes in the level of blur imposed by HOA regardless of which observer’s HOA was used to generate the stimuli, with the perceived neutral point proportional to the amount of blur in the adapting image.

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Neural adjustments to chromatic blur

Cited by 16 publications

References 44 publications

Visual Adaptation

Visual Adaptation

Response normalization and blur adaptation: Data and multi-scale model

Adapting to blur produced by ocular high-order aberrations

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